
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


__lowerCAmelCase     :	List[str]       			=  logging.getLogger(__name__)

def a__       (			A_=2,	A_=3,	A_=16,	A_ = 10,	A_ = 2   ):






  '''simple docstring'''





  def get_dataset(A_   ):
    __magic_name__								=			torch.randn(batch_size * n_batches,	1   )
    return TensorDataset(A_,	a * x + b + 0.1 * torch.randn(batch_size * n_batches,	1   )   )

  __magic_name__								=			get_dataset(A_   )
  __magic_name__								=			get_dataset(A_   )
  __magic_name__								=			DataLoader(A_,	shuffle=A_,	batch_size=A_,	num_workers=4   )
  __magic_name__								=			DataLoader(A_,	shuffle=A_,	batch_size=A_,	num_workers=4   )
  return (train_dataloader, valid_dataloader)

def a__       (			A_,	A_,	A_,	A_,	A_,	A_=None   ):






  '''simple docstring'''





  __magic_name__								=			[]
  for epoch in range(A_   ):
    # Train quickly
    model.train()
    for batch in dataloader:
      __magic_name__	,       __magic_name__								=			batch
      __magic_name__								=			model(A_   )
      __magic_name__								=			torch.nn.functional.mse_loss(A_,	A_   )
      accelerator.backward(A_   )
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random()   )  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  def __init__(					self     :					int       )     ->      List[str]:







    """simple docstring"""






    super().__init__()
    __magic_name__								=			nn.Parameter(torch.randn(1       )       )
    __magic_name__								=			nn.Parameter(torch.randn(1       )       )





  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    return x * self.a + self.b







class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Tuple       )     ->      str:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(42       )
      __magic_name__								=			DummyModel()
      __magic_name__								=			torch.optim.Adam(params=model.parameters()     ,				lr=1E-3       )
      __magic_name__	,       __magic_name__								=			dummy_dataloaders()
      __magic_name__								=			ProjectConfiguration(total_limit=1     ,				project_dir=UpperCamelCase__     ,				automatic_checkpoint_naming=UpperCamelCase__       )
      # Train baseline
      __magic_name__								=			Accelerator(project_config=UpperCamelCase__       )
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			accelerator.prepare(
          UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      # Save initial
      accelerator.save_state()

      # Save second state
      accelerator.save_state()
      self.assertEqual(len(os.listdir(accelerator.project_dir       )       )     ,				1       )
  def 				_lowercase				(					self     :					int       )     ->      List[str]:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(42       )
      __magic_name__								=			DummyModel()
      __magic_name__								=			torch.optim.Adam(params=model.parameters()     ,				lr=1E-3       )
      __magic_name__	,       __magic_name__								=			dummy_dataloaders()
      # Train baseline
      __magic_name__								=			Accelerator()
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			accelerator.prepare(
          UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      # Save initial
      __magic_name__								=			os.path.join(UpperCamelCase__     ,				"""initial"""       )
      accelerator.save_state(UpperCamelCase__       )
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()
      __magic_name__								=			train(3     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()

      # Train partially
      set_seed(42       )
      __magic_name__								=			DummyModel()
      __magic_name__								=			torch.optim.Adam(params=model.parameters()     ,				lr=1E-3       )
      __magic_name__	,       __magic_name__								=			dummy_dataloaders()
      __magic_name__								=			Accelerator()
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			accelerator.prepare(
          UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      accelerator.load_state(UpperCamelCase__       )
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )

      __magic_name__								=			train(2     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      # Save everything
      __magic_name__								=			os.path.join(UpperCamelCase__     ,				"""checkpoint"""       )
      accelerator.save_state(UpperCamelCase__       )

      # Load everything back in and make sure all states work
      accelerator.load_state(UpperCamelCase__       )
      test_rands += train(1     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					str       )     ->      Union[str, Any]:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(42       )
      __magic_name__								=			DummyModel()
      __magic_name__								=			torch.optim.Adam(params=model.parameters()     ,				lr=1E-3       )
      __magic_name__	,       __magic_name__								=			dummy_dataloaders()
      __magic_name__								=			ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__       )

      # Train baseline
      __magic_name__								=			Accelerator(project_dir=UpperCamelCase__     ,				project_config=UpperCamelCase__       )
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			accelerator.prepare(
          UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      # Save initial
      accelerator.save_state()
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()
      __magic_name__								=			train(3     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()

      # Train partially
      set_seed(42       )
      __magic_name__								=			DummyModel()
      __magic_name__								=			torch.optim.Adam(params=model.parameters()     ,				lr=1E-3       )
      __magic_name__	,       __magic_name__								=			dummy_dataloaders()
      __magic_name__								=			ProjectConfiguration(iteration=1     ,				automatic_checkpoint_naming=UpperCamelCase__       )
      __magic_name__								=			Accelerator(project_dir=UpperCamelCase__     ,				project_config=UpperCamelCase__       )
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			accelerator.prepare(
          UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      accelerator.load_state(os.path.join(UpperCamelCase__     ,				"""checkpoints"""     ,				"""checkpoint_0"""       )       )
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )

      __magic_name__								=			train(2     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      # Save everything
      accelerator.save_state()

      # Load everything back in and make sure all states work
      accelerator.load_state(os.path.join(UpperCamelCase__     ,				"""checkpoints"""     ,				"""checkpoint_1"""       )       )
      test_rands += train(1     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      ((__magic_name__)	,       (__magic_name__))								=			model.a.item(), model.b.item()
      __magic_name__								=			optimizer.state_dict()
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			torch.tensor([1, 2, 3]       )
    __magic_name__								=			torch.tensor([2, 3, 4]       )
    __magic_name__								=			DummyModel()
    __magic_name__								=			torch.optim.Adam(net.parameters()       )
    __magic_name__								=			Accelerator()
    with self.assertRaises(UpperCamelCase__       ) as ve:
      accelerator.register_for_checkpointing(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
    __magic_name__								=			str(ve.exception       )
    self.assertTrue("""Item at index 0""" in message       )
    self.assertTrue("""Item at index 1""" in message       )
    self.assertFalse("""Item at index 2""" in message       )
    self.assertFalse("""Item at index 3""" in message       )
  def 				_lowercase				(					self     :					Dict       )     ->      Dict:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(42       )
      __magic_name__								=			DummyModel()
      __magic_name__								=			torch.optim.Adam(params=model.parameters()     ,				lr=1E-3       )
      __magic_name__								=			torch.optim.lr_scheduler.StepLR(UpperCamelCase__     ,				step_size=1     ,				gamma=0.99       )
      __magic_name__	,       __magic_name__								=			dummy_dataloaders()
      __magic_name__								=			ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__       )
      # Train baseline
      __magic_name__								=			Accelerator(project_dir=UpperCamelCase__     ,				project_config=UpperCamelCase__       )
      __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			accelerator.prepare(
          UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      # Save initial
      accelerator.save_state()
      __magic_name__								=			scheduler.state_dict()
      train(3     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
      self.assertNotEqual(UpperCamelCase__     ,				scheduler.state_dict()       )

      # Load everything back in and make sure all states work
      accelerator.load_state(os.path.join(UpperCamelCase__     ,				"""checkpoints"""     ,				"""checkpoint_0"""       )       )
      self.assertEqual(UpperCamelCase__     ,				scheduler.state_dict()       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Optional[Any]:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as tmpdir:
      set_seed(42       )
      __magic_name__								=			DummyModel()
      __magic_name__								=			ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__     ,				total_limit=2       )
      # Train baseline
      __magic_name__								=			Accelerator(project_dir=UpperCamelCase__     ,				project_config=UpperCamelCase__       )
      __magic_name__								=			accelerator.prepare(UpperCamelCase__       )
      # Save 3 states:
      for _ in range(11       ):
        accelerator.save_state()
      self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase__     ,				"""checkpoints"""     ,				"""checkpoint_0"""       )       )       )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__     ,				"""checkpoints"""     ,				"""checkpoint_9"""       )       )       )
      self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__     ,				"""checkpoints"""     ,				"""checkpoint_10"""       )       )       )





  @require_cuda
  def 				_lowercase				(					self     :					Any       )     ->      int:







    """simple docstring"""






    __magic_name__								=			["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__       )]
    execute_subprocess_async(UpperCamelCase__     ,				env=os.environ.copy()       )


if __name__ == "__main__":
    __lowerCAmelCase     :	str       			=  '/tmp/accelerate/state_checkpointing'
    __lowerCAmelCase     :	Optional[Any]       			=  DummyModel()
    __lowerCAmelCase     :	str       			=  torch.optim.Adam(params=model.parameters(), lr=1e-3)
    __lowerCAmelCase     :	int       			=  torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
    __lowerCAmelCase			, __lowerCAmelCase     :	Union[str, Any]       			=  dummy_dataloaders()
    __lowerCAmelCase     :	str       			=  ProjectConfiguration(automatic_checkpoint_naming=True)
    # Train baseline
    __lowerCAmelCase     :	str       			=  Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
    if accelerator.process_index == 0:
        if os.path.exists(savedir):
            shutil.rmtree(savedir)
        os.makedirs(savedir)
    __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase     :	Any       			=  accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, scheduler
    )
    __lowerCAmelCase			, __lowerCAmelCase     :	Any       			=  accelerator.prepare(model, optimizer)
    train(3, model, train_dataloader, optimizer, accelerator, scheduler)
    # Check that the intial optimizer is loaded on the GPU
    for group in optimizer.param_groups:
        __lowerCAmelCase     :	str       			=  group['params'][0].device
        break
    assert param_device.type == accelerator.device.type
    __lowerCAmelCase     :	Any       			=  model.cpu()
    accelerator.wait_for_everyone()
    accelerator.save_state()
    accelerator.wait_for_everyone()

    # Check CPU state
    accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
    for group in optimizer.param_groups:
        __lowerCAmelCase     :	List[str]       			=  group['params'][0].device
        break
    assert (
        param_device.type == torch.device('cpu').type
    ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

    # Check device state
    model.to(accelerator.device)
    accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
    for group in optimizer.param_groups:
        __lowerCAmelCase     :	Union[str, Any]       			=  group['params'][0].device
        break
    assert (
        param_device.type == accelerator.device.type
    ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

    # Check error
    with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
        accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
    accelerator.wait_for_everyone()
    if accelerator.process_index == 0:
        shutil.rmtree(savedir)
    accelerator.wait_for_everyone()



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						["""image_processor""", """tokenizer"""]
  a__       			=						"""BridgeTowerImageProcessor"""
  a__       			=						("""RobertaTokenizer""", """RobertaTokenizerFast""")
  def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    super().__init__(UpperCamelCase__     ,				UpperCamelCase__       )
  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Union[bool, str, PaddingStrategy] = False     ,				UpperCamelCase__     :					Union[bool, str, TruncationStrategy] = None     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					int = 0     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = False     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				**UpperCamelCase__     :					Dict     ,				)     ->      BatchEncoding:







    """simple docstring"""






    __magic_name__								=			self.tokenizer(
        text=UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				stride=UpperCamelCase__     ,				pad_to_multiple_of=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				return_overflowing_tokens=UpperCamelCase__     ,				return_special_tokens_mask=UpperCamelCase__     ,				return_offsets_mapping=UpperCamelCase__     ,				return_length=UpperCamelCase__     ,				verbose=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    # add pixel_values + pixel_mask
    __magic_name__								=			self.image_processor(
        UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				do_normalize=UpperCamelCase__     ,				do_center_crop=UpperCamelCase__     ,				**UpperCamelCase__       )
    encoding.update(UpperCamelCase__       )

    return encoding
  def 				_lowercase				(					self     :					Optional[Any]     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					int     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__     ,				**UpperCamelCase__       )





  @property
  def 				_lowercase				(					self     :					Tuple       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer.model_input_names
    __magic_name__								=			self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

